# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

from typing import (
    TYPE_CHECKING,
    TypedDict,
)

from typing_extensions import NotRequired, Unpack

import paddle
import paddle.nn.functional as F
from paddle import nn
from paddle.base.param_attr import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, Conv2D, Dropout, MaxPool2D
from paddle.utils.download import get_weights_path_from_url

if TYPE_CHECKING:
    from paddle import Tensor
    from paddle._typing import Size2

    class _SqueezeNetOptions(TypedDict):
        num_classes: NotRequired[int]
        with_pool: NotRequired[bool]


__all__ = []

model_urls = {
    'squeezenet1_0': (
        'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams',
        '30b95af60a2178f03cf9b66cd77e1db1',
    ),
    'squeezenet1_1': (
        'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams',
        'a11250d3a1f91d7131fd095ebbf09eee',
    ),
}


class MakeFireConv(nn.Layer):
    def __init__(
        self,
        input_channels: int,
        output_channels: int,
        filter_size: Size2,
        padding: Size2 = 0,
    ) -> None:
        super().__init__()
        self._conv = Conv2D(
            input_channels,
            output_channels,
            filter_size,
            padding=padding,
            weight_attr=ParamAttr(),
            bias_attr=ParamAttr(),
        )

    def forward(self, x: Tensor) -> Tensor:
        x = self._conv(x)
        x = F.relu(x)
        return x


class MakeFire(nn.Layer):
    def __init__(
        self,
        input_channels: int,
        squeeze_channels: int,
        expand1x1_channels: int,
        expand3x3_channels: int,
    ) -> None:
        super().__init__()
        self._conv = MakeFireConv(input_channels, squeeze_channels, 1)
        self._conv_path1 = MakeFireConv(squeeze_channels, expand1x1_channels, 1)
        self._conv_path2 = MakeFireConv(
            squeeze_channels, expand3x3_channels, 3, padding=1
        )

    def forward(self, inputs: Tensor) -> Tensor:
        x = self._conv(inputs)
        x1 = self._conv_path1(x)
        x2 = self._conv_path2(x)
        return paddle.concat([x1, x2], axis=1)


class SqueezeNet(nn.Layer):
    """SqueezeNet model from
    `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
    <https://arxiv.org/pdf/1602.07360.pdf>`_.

    Args:
        version (str): Version of SqueezeNet, which can be "1.0" or "1.1".
        num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
                            will not be defined. Default: 1000.
        with_pool (bool, optional): Use pool before the last fc layer or not. Default: True.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of SqueezeNet model.

    Examples:
        .. code-block:: python

            >>> import paddle
            >>> from paddle.vision.models import SqueezeNet

            >>> # build v1.0 model
            >>> model = SqueezeNet(version='1.0')

            >>> # build v1.1 model
            >>> # model = SqueezeNet(version='1.1')

            >>> x = paddle.rand([1, 3, 224, 224])
            >>> out = model(x)

            >>> print(out.shape)
            [1, 1000]
    """

    version: str
    num_classes: int
    with_pool: bool

    def __init__(
        self, version: str, num_classes: int = 1000, with_pool: bool = True
    ) -> None:
        super().__init__()
        self.version = version
        self.num_classes = num_classes
        self.with_pool = with_pool

        supported_versions = ['1.0', '1.1']
        assert version in supported_versions, (
            f"supported versions are {supported_versions} but input version is {version}"
        )

        if self.version == "1.0":
            self._conv = Conv2D(
                3,
                96,
                7,
                stride=2,
                weight_attr=ParamAttr(),
                bias_attr=ParamAttr(),
            )
            self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
            self._conv1 = MakeFire(96, 16, 64, 64)
            self._conv2 = MakeFire(128, 16, 64, 64)
            self._conv3 = MakeFire(128, 32, 128, 128)
            self._conv4 = MakeFire(256, 32, 128, 128)
            self._conv5 = MakeFire(256, 48, 192, 192)
            self._conv6 = MakeFire(384, 48, 192, 192)
            self._conv7 = MakeFire(384, 64, 256, 256)
            self._conv8 = MakeFire(512, 64, 256, 256)
        else:
            self._conv = Conv2D(
                3,
                64,
                3,
                stride=2,
                padding=1,
                weight_attr=ParamAttr(),
                bias_attr=ParamAttr(),
            )
            self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
            self._conv1 = MakeFire(64, 16, 64, 64)
            self._conv2 = MakeFire(128, 16, 64, 64)
            self._conv3 = MakeFire(128, 32, 128, 128)
            self._conv4 = MakeFire(256, 32, 128, 128)
            self._conv5 = MakeFire(256, 48, 192, 192)
            self._conv6 = MakeFire(384, 48, 192, 192)
            self._conv7 = MakeFire(384, 64, 256, 256)
            self._conv8 = MakeFire(512, 64, 256, 256)

        self._drop = Dropout(p=0.5, mode="downscale_in_infer")
        self._conv9 = Conv2D(
            512, num_classes, 1, weight_attr=ParamAttr(), bias_attr=ParamAttr()
        )
        self._avg_pool = AdaptiveAvgPool2D(1)

    def forward(self, inputs: Tensor) -> Tensor:
        x = self._conv(inputs)
        x = F.relu(x)
        x = self._pool(x)
        if self.version == "1.0":
            x = self._conv1(x)
            x = self._conv2(x)
            x = self._conv3(x)
            x = self._pool(x)
            x = self._conv4(x)
            x = self._conv5(x)
            x = self._conv6(x)
            x = self._conv7(x)
            x = self._pool(x)
            x = self._conv8(x)
        else:
            x = self._conv1(x)
            x = self._conv2(x)
            x = self._pool(x)
            x = self._conv3(x)
            x = self._conv4(x)
            x = self._pool(x)
            x = self._conv5(x)
            x = self._conv6(x)
            x = self._conv7(x)
            x = self._conv8(x)
        if self.num_classes > 0:
            x = self._drop(x)
            x = self._conv9(x)
        if self.with_pool:
            x = F.relu(x)
            x = self._avg_pool(x)
            x = paddle.squeeze(x, axis=[2, 3])

        return x


def _squeezenet(
    arch: str,
    version: str,
    pretrained: bool,
    **kwargs: Unpack[_SqueezeNetOptions],
) -> SqueezeNet:
    model = SqueezeNet(version, **kwargs)
    if pretrained:
        assert arch in model_urls, (
            f"{arch} model do not have a pretrained model now, you should set pretrained=False"
        )
        weight_path = get_weights_path_from_url(
            model_urls[arch][0], model_urls[arch][1]
        )
        param = paddle.load(weight_path)
        model.set_dict(param)

    return model


def squeezenet1_0(
    pretrained: bool = False, **kwargs: Unpack[_SqueezeNetOptions]
) -> SqueezeNet:
    """SqueezeNet v1.0 model from
    `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
    <https://arxiv.org/pdf/1602.07360.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`SqueezeNet <api_paddle_vision_models_SqueezeNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of SqueezeNet v1.0 model.

    Examples:
        .. code-block:: python

            >>> import paddle
            >>> from paddle.vision.models import squeezenet1_0

            >>> # build model
            >>> model = squeezenet1_0()

            >>> # build model and load imagenet pretrained weight
            >>> # model = squeezenet1_0(pretrained=True)

            >>> x = paddle.rand([1, 3, 224, 224])
            >>> out = model(x)

            >>> print(out.shape)
            [1, 1000]
    """
    return _squeezenet('squeezenet1_0', '1.0', pretrained, **kwargs)


def squeezenet1_1(
    pretrained: bool = False, **kwargs: Unpack[_SqueezeNetOptions]
) -> SqueezeNet:
    """SqueezeNet v1.1 model from
    `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
    <https://arxiv.org/pdf/1602.07360.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`SqueezeNet <api_paddle_vision_models_SqueezeNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of SqueezeNet v1.1 model.

    Examples:
        .. code-block:: python

            >>> import paddle
            >>> from paddle.vision.models import squeezenet1_1

            >>> # build model
            >>> model = squeezenet1_1()

            >>> # build model and load imagenet pretrained weight
            >>> # model = squeezenet1_1(pretrained=True)

            >>> x = paddle.rand([1, 3, 224, 224])
            >>> out = model(x)

            >>> print(out.shape)
            [1, 1000]
    """
    return _squeezenet('squeezenet1_1', '1.1', pretrained, **kwargs)